Fires in NYC and FDNY Response

Tasks

1. Location of Severe Fires

Provide a leaflet map of the highest severity fires (i.e. subset to the highest category in HIGHEST_LEVEL_DESC) contained in the file buiding_fires.csv. Ignore locations that fall outside the five boroughs of New York City. Provide at least three pieces of information on the incident in a popup.

2. Layers and Clusters

a) Color by Type of Property

Start with the previous map. Now, distinguish the markers of the fire locations by PROPERTY_USE_DESC, i.e. what kind of property was affected. If there are too many categories, collapse some categories. Choose an appropriate coloring scheme to map the locations by type of affected property. Add a legend informing the user about the color scheme. Also make sure that the information about the type of affected property is now contained in the popup information. Show this map.

b) Cluster

Add marker clustering, so that zooming in will reveal the individual locations but the zoomed out map only shows the clusters. Show the map with clusters.

3. Fire Houses

The second data file contains the locations of the 218 firehouses in New York City. Start with the non-clustered map (2b) and now adjust the size of the circle markers by severity (TOTAL_INCIDENT_DURATION or UNITS_ONSCENE seem plausible options). More severe incidents should have larger circles on the map. On the map, also add the locations of the fire houses. Add two layers (“Incidents”, “Firehouses”) that allow the user to select which information to show.

4. Distance from Firehouse and Response Time

We now want to investigate whether the distance of the incident from the nearest firehouse varies across the city.

a) Calculate Distance

For all incident locations (independent of severity), identify the nearest firehouse and calculate the distance between the firehouse and the incident location. Provide a scatter plot showing the time until the first engine arrived (the variables INCIDENT_DATE_TIME and ARRIVAL_DATE_TIME) will be helpful.

Note :Arrival date time is missing for 21 incidents.

Note :There are multiple outliers which are making it difficult to see the trend.

Removing the outliers for a better graph.

Now also visualize the patterns separately for severe and non-severe incidents (use HIGHEST_LEVEL_DESC but feel free to reduce the number of categories). What do you find?

The slopes help compare if the incident severity matters in the relation between arrival time and the incident distance.

Second Alarm incidences have the highest slope but there are lesser number of incidences for second alarm.

Highest alarm incidences have a higher slope than first alarm incidences. This implies that the first fire engine reaches similar distance faster if the incidence is of highest severity compared to first alarm severity.

b) Map of Response Times

Provide a map visualization of response times. Investigate whether the type of property affected (PROPERTY_USE_DESC) or fire severity (HIGHEST_LEVEL_DESC) play a role here.

1. Type of property
2. Severity levels

Show a faceted choropleth map indicating how response times have developed over the years. What do you find?

Over the years Bronx has the lowest average response times. However, it seems that the overall average response times have increased. In 2013, the highest average response time was 3.86 minutes only 45 seconds more than the lowest average response time. Whereas in 2018, the lowest average response time has increased to 3.41 minutes and the highest average response time has increased to 4.15 minutes. The difference between the lowest and the highest has decreased however to just a little over 20 seconds now.